Title: LamiGauss: Pitching Radiative Gaussian for Sparse-View X-ray Laminography Reconstruction Show Chinese title

Title: LamiGauss：用于稀疏视图X射线层析成像重建的辐射高斯分布

Abstract: X-ray Computed Laminography (CL) is essential for non-destructive inspection of plate-like structures in applications such as microchips and composite battery materials, where traditional computed tomography (CT) struggles due to geometric constraints. However, reconstructing high-quality volumes from laminographic projections remains challenging, particularly under highly sparse-view acquisition conditions. In this paper, we propose a reconstruction algorithm, namely LamiGauss, that combines Gaussian Splatting radiative rasterization with a dedicated detector-to-world transformation model incorporating the laminographic tilt angle. LamiGauss leverages an initialization strategy that explicitly filters out common laminographic artifacts from the preliminary reconstruction, preventing redundant Gaussians from being allocated to false structures and thereby concentrating model capacity on representing the genuine object. Our approach effectively optimizes directly from sparse projections, enabling accurate and efficient reconstruction with limited data. Extensive experiments on both synthetic and real datasets demonstrate the effectiveness and superiority of the proposed method over existing techniques. LamiGauss uses only 3$\%$ of full views to achieve superior performance over the iterative method optimized on a full dataset. Show Chinese abstract

Abstract: X射线计算机层析成像（CL）对于微芯片和复合电池材料等应用中板状结构的无损检测至关重要，由于几何限制，传统计算机断层扫描（CT）在此类情况下表现不佳。 然而，在高度稀疏视角采集条件下，从层析投影中重建高质量体积仍然具有挑战性。 在本文中，我们提出了一种重建算法，即LamiGauss，该算法结合了高斯点云辐射光栅化与专用的探测器到世界坐标变换模型，该模型包含层析成像倾斜角。 LamiGauss利用一种初始化策略，从初步重建中明确过滤掉常见的层析成像伪影，防止冗余的高斯分布在虚假结构上，从而将模型能力集中在表示真实物体上。 我们的方法可以直接从稀疏投影进行优化，实现了在有限数据下的准确高效重建。 在合成和真实数据集上的大量实验表明，所提出的方法比现有技术更有效且更优越。 LamiGauss仅使用3$\%$的完整视角，就能在全数据集上优化的迭代方法上取得更优性能。